Project Summary Our understanding of human disease mechanisms and therapies is crucially illuminated by information discovered in model systems. Although numerous resources exist that collect information about model organisms, their very multiplicity makes it difficult for researchers and clinicians to effectively utilize such resources, since each one focuses on a different species, disease, or data type. Furthermore, models do not faithfully recapitulate all aspects of disease pathology, and we need better methods for identifying and designing disease models that focus on elucidating aspects of phenotype and pathophysiology that are most relevant to human disease. The goal of the proposed work is to facilitate disease research by integrating a large diversity of disconnected resources so that we can make better use of existing model organisms and in vitro models. The Monarch Initiative is a global, translational consortium that provides sophisticated algorithms for phenotype comparison within and across species that leverage a large corpus of deeply integrated human and model data. For instance, our Exomiser tool supports the clinical community by prioritizing exomic variants according to variant pathogenicity and phenotypic relevance to support disease-gene discovery. Here we propose to extend the range and precision of Monarch disease modeling by including a greater diversity of species and sources that focus on a broader range of diseases, as well as new categories of clinical data such as disease progression and quantitative measurements. We will utilize these data in our enhanced algorithms that will include temporal ordering of phenotypes, absence of phenotypes, and more model organisms, to provide greater specificity of model-disease matching. Finally, we will make this large, semantically integrated landscape of human and model data navigable by end-users within our website and by community distribution of visualization tools. One example is PhenoGrid, which reveals phenotypic commonalities shared across model, disease, or patients against all other models and diseases in a succinct visual matrix. Finally, we will further assist external resources by prospectively assisting others to provide data compliant to Global Alliance for Genomics and Health and organism identification community standards.